[R] loop issues (r.squared)

[roger koenker]

both Matrix and SparseM have formats of this type.

url:    www.econ.uiuc.edu/~roger                Roger Koenker
email   rkoenker at uiuc.edu                       Department of Economics
vox:    217-333-4558                            University of Illinois
fax:    217-244-6678                            Champaign, IL 61820


On Feb 8, 2007, at 4:45 PM, andy1983 wrote:

>
> That was a neat trick. However, it created a new problem.
>
> Before, it took way too long for a 10,000 columns to finish.
>
> Now, I test the memory limit. With 10,000 columns, I use up about  
> 1.5 GBs.
>
> Assuming memory is not the issue, I still end up with a huge matrix  
> that is
> difficult to export. Is there a way to convert it to 3 columns (1  
> for row, 1
> for column, 1 for value)?
>
> Thanks.
>
>
>
> Greg Snow wrote:
>>
>> The most straight forward way that I can think of is just:
>>
>>> cor(my.mat)^2 # assuming my.mat is the matrix with your data in the
>> columns
>>
>> That will give you all the R^2 values for regressing 1 column on 1
>> column (it is called R-squared for a reason).
>>
>>
>>> I would like to compare every column in my matrix with every
>>> other column and get the r-squared. I have been using the
>>> following formula and loops:
>>> summary(lm(matrix[,x]~matrix[,y]))$r.squared
>>> where x and y are the looping column numbers
>>>
>>> If I have 100 columns (10,000 iterations), the loops give me
>>> results in a reasonable time.
>>> If I try 10,000 columns, the loops take forever even if there
>>> is no formula inside. I am guessing I can vectorize my code
>>> so that I could eliminate one or both loops. Unfortunately, I
>>> can't figure out how to.
>>
>>
>
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